Hermano Bernardo

Abstract
This work examines the impact of time-slice resolution on renewable energy integration outcomes in long-term energy planning using OSeMOSYS. The analysis focuses on the Portuguese power system over the period 2024-2050, analysed under three scenarios: one coarse (six time slices) and two finer (twelve and twenty-four time slices), all evaluated under strict cost optimisation. Key outputs include system costs, technology deployment, dispatch behaviour, and emissions trajectories. Results indicate that temporal structure directly shapes long-term planning outcomes. The coarse scenario smooths short-term variability and promotes investment in technologies, particularly solar photovoltaic and wind, while reducing the share of natural gas combined cycle (NGCC), presenting an optimistic decarbonisation pathway. Finer resolutions capture intra-day and seasonal fluctuations, revealing operational constraints, increasing NGCC capacity (1.3 to 2 GW), and moderating Solar PV and wind output. Overall, the findings demonstrate that temporal resolution is not a secondary modelling choice but a critical determinant of the credibility of long-term energy planning. Appropriate temporal segmentation is therefore essential for robust evaluation of policy options, system flexibility requirements, and sustainable energy transition strategies © 2026 IEEE.

Abstract

Abstract
To achieve carbon neutrality by 2050, commercial buildings have installed photovoltaic systems to reduce carbon emissions and operational costs. Nevertheless, PV generation does not always match the building’s energy demand profile, therefore storage systems are needed to store excess energy and supply it when necessary. This paper presents a Mixed Integer Linear Programming optimisation algorithm designed to schedule the operation of the electric storage system, aiming to minimise the building’s energy-related costs. An annual hourly simulation of the optimised system was performed to assess the cost reduction. To prevent excessive operation of the electric storage system, an approach to penalise low energy charging was studied, with results showing a significant increase in the system’s lifespan.

Abstract
In recent years, non-residential buildings have increasingly adopted renewable energy generation systems to align with the European Union's goal of achieving carbon neutrality by 2050. However, energy storage systems play a fundamental role in maximising the use of the generated renewable energy. Due to their high acquisition costs, adequately sizing these systems is essential. Moreover, applying an optimal scheduling strategy for energy storage operation can significantly improve the economic viability of such systems by reducing energy-related costs. In this paper, a MILP-based optimisation algorithm-incorporating battery lifespan constraints-is applied to a reference commercial building to schedule the operation of the storage system. A sensitivity analysis on the installed photovoltaic power and energy storage capacity is performed to evaluate their impact on the economic and operational performance of the optimisation algorithm under different sizing configurations.

Abstract
Recent studies have focused on forecasting power demand in buildings to enhance energy management. However, the literature still lacks comparative analyses of power demand forecasting algorithms. In addition, more case studies involving different building typologies are needed, as each building exhibits distinct behavior and load profiles. This paper aims to develop machine learning models to forecast the power demand of a large shopping center in the northern region of Portugal. The main objective is to compare the performance of several machine learning models. The results are promising, demonstrating adequate performance even during most holidays.